Improved machine learning leak fault recognition for low-pressure natural gas valve

Abstract

Monitoring valve operation status is very significant in saving natural gas resources and realizing sustainability of the fossil energy. At present, many machine learning algorithms have been applied to nondestructive testing (NDT) field. Among them, the twin support vector machine (TWSVM) is a representative binary classification method. However, the traditional TWSVM assigns same classification weights to all sample points, including classification boundary points and other non-boundary sample points. These same classification weights often lead to classification errors or overfitting in TWSVM. Therefore, to overcome the drawback, we propose an improved nonlinear TWSVM (I-TWSVM) to optimize the classification weights assignment processing. In new assignment processing, the boundary points are given greater classification weights than other non-boundary points, which makes the I-TWSVM becomes more sensitive than the traditional TWSVM to boundary points. The I-TWSVM has been applied to recognize the acoustic emission (AE) sample data for valve internal leak fault accident. The leak recognition experiment revealed that the proposed algorithm is better than the traditional nonlinear TWSVM in classification accuracy and sensitivity, and its calculation time is faster than that of nonlinear TWSVM, which can promote the nondestructive testing technology development for low-pressure natural gas valve.